Daily COVID-19 Cases Research Articles

SARS-CoV-2 RNA surveillance in large to small centralized wastewater treatment plants preceding the third COVID-19 resurgence in Bangkok, Thailand

Wastewater surveillance for SARS-CoV-2 RNA has been a successful indicator of COVID-19 outbreaks in populations prior to clinical testing. However, this has been mostly conducted in high-income countries, which means there is a dearth of performance investigations in low- and middle-income countries with different socio-economic settings. This study evaluated the applicability of SARS-CoV-2 RNA monitoring in wastewater (n = 132) to inform COVID-19 infection in the city of Bangkok, Thailand using CDC N1 and N2 RT-qPCR assays. Wastewater influents (n = 112) and effluents (n = 20) were collected from 19 centralized wastewater treatment plants (WWTPs) comprising four large, four medium, and 11 small WWTPs during seven sampling events from January to April 2021 prior to the third COVID-19 resurgence that was officially declared in April 2021. The CDC N1 assay showed higher detection rates and mostly lower Ct values than the CDC N2. SARS-CoV-2 RNA was first detected at the first event when new reported cases were low. Increased positive detection rates preceded an increase in the number of newly reported cases and increased over time with the reported infection incidence. Wastewater surveillance (both positive rates and viral loads) showed strongest correlation with daily new COVID-19 cases at 22–24 days lag (Spearman's Rho = 0.85–1.00). Large WWTPs (serving 432,000–580,000 of the population) exhibited similar trends of viral loads and new cases to those from all 19 WWTPs, emphasizing that routine monitoring of the four large WWTPs could provide sufficient information for the city-scale dynamics. Higher sampling frequency at fewer sites, i.e., at the four representative WWTPs, is therefore suggested especially during the subsiding period of the outbreak to indicate the prevalence of COVID-19 infection, acting as an early warning of COVID-19 resurgence.

Effects of temperature and relative humidity on the COVID-19 pandemic in different climates: a study across some regions in Algeria (North Africa).

After more than a year from the first confirmed cases of coronavirus (COVID-19) disease, the role of meteorological factors in the transmission of the virus still needs to be correctly determined. In this scenario of deep uncertainty, the present study aims to investigate the effects of temperature and relative humidity on daily new cases of COVID-19. For this purpose, the COVID-19’s development of infection in fourteen Algerian cities characterized by different climatic conditions, during the period from April 1, 2020, to August 31, 2020, has been investigated. A detailed time series analysis along with linear regression was used to state a possible correlation among some climate’s factor variability (temperature and relative humidity) and daily new confirmed cases of COVID-19. The results showed a weak correlation between daily new cases of COVID-19 and meteorological factors throughout the selected regions. In addition, we concluded that the COVID-19 could fit to high or low values of temperature and relative humidity, and other factors not climates could affect the spreading of the virus like demography and human contact. So, after the discovery of the vaccine and before vaccination of 70% of the world’s population, living with the virus has become an inevitable reality, and it is mandatory to apply the sanitary procedures to slow down the COVID-19 transmission.Graphical abstract

The outbreak of COVID-19 in Taiwan in late spring 2021: combinations of specific weather conditions and related factors

This study aimed to investigate the impact of weather conditions on the daily incidence of the COVID-19 pandemic in late spring 2021 in Taiwan, which is unlike the weather conditions of the COVID-19 outbreak in 2020. Meteorological parameters such as maximum daily temperature, relative humidity, and wind speed were included. The Spearman rank correlation test was used to evaluate the relationship between weather and daily domestic COVID-19 cases. The maximum daily temperature had a positively significant correlation with daily new COVID-19 cases within a 14-day lag period, while the relative humidity and wind speed has a fairly high correlation with the number of daily cases within a 13- and 14-day lag, respectively. In addition, the weather characteristics during this period were an increasingly high temperature, with steady high relative humidity and slightly decreasing wind speed. Our study revealed the weather conditions at the time of the domestic outbreak of COVID-19 in Taiwan in May 2021 and the possible association between weather factors and the COVID-19 pandemic. Further large-scale analysis of weather factors is essential for understanding the impact of weather on the spread of infectious diseases.

National and State Trends in Anxiety and Depression Severity Scores Among Adults During the COVID-19 Pandemic - United States, 2020-2021.

Recent studies indicate an increase in the percentage of adults who reported clinically relevant symptoms of anxiety and depression during the COVID-19 pandemic (1-3). For example, based on U.S. Census Bureau Household Pulse Survey (HPS) data, CDC reported significant increases in symptoms of anxiety and depressive disorders among adults aged ≥18 years during August 19, 2020-February 1, 2021, with the largest increases among adults aged 18-29 years and among those with less than a high school education (1). To assess more recent national trends, as well as state-specific trends, CDC used HPS data (4) to assess trends in reported anxiety and depression among U.S. adults in all 50 states and the District of Columbia (DC) during August 19, 2020-June 7, 2021 (1). Nationally, the average anxiety severity score increased 13% from August 19-31, 2020, to December 9-21, 2020 (average percent change [APC] per survey wave=1.5%) and then decreased 26.8% from December 9-21, 2020, to May 26-June 7, 2021 (APC=-3.1%). The average depression severity score increased 14.8% from August 19-31, 2020, to December 9-21, 2020 (APC=1.7%) and then decreased 24.8% from December 9-21, 2020, to May 26-June 7, 2021 (APC=-2.8%). State-specific trends were generally similar to national trends, with both anxiety and depression scores for most states peaking during the December 9-21, 2020, or January 6-18, 2021, survey waves. Across the entire study period, the frequency of anxiety and depression symptoms was positively correlated with the average number of daily COVID-19 cases. Mental health services and resources, including telehealth behavioral services, are critical during the COVID-19 pandemic.

Tracking Self-reported Symptoms and Medical Conditions on Social Media During the COVID-19 Pandemic: Infodemiological Study.

BackgroundHarnessing health-related data posted on social media in real time can offer insights into how the pandemic impacts the mental health and general well-being of individuals and populations over time.ObjectiveThis study aimed to obtain information on symptoms and medical conditions self-reported by non-Twitter social media users during the COVID-19 pandemic, to determine how discussion of these symptoms and medical conditions changed over time, and to identify correlations between frequency of the top 5 commonly mentioned symptoms post and daily COVID-19 statistics (new cases, new deaths, new active cases, and new recovered cases) in the United States.MethodsWe used natural language processing (NLP) algorithms to identify symptom- and medical condition–related topics being discussed on social media between June 14 and December 13, 2020. The sample posts were geotagged by NetBase, a third-party data provider. We calculated the positive predictive value and sensitivity to validate the classification of posts. We also assessed the frequency of health-related discussions on social media over time during the study period, and used Pearson correlation coefficients to identify statistically significant correlations between the frequency of the 5 most commonly mentioned symptoms and fluctuation of daily US COVID-19 statistics.ResultsWithin a total of 9,807,813 posts (nearly 70% were sourced from the United States), we identified a discussion of 120 symptom-related topics and 1542 medical condition–related topics. Our classification of the health-related posts had a positive predictive value of over 80% and an average classification rate of 92% sensitivity. The 5 most commonly mentioned symptoms on social media during the study period were anxiety (in 201,303 posts or 12.2% of the total posts mentioning symptoms), generalized pain (189,673, 11.5%), weight loss (95,793, 5.8%), fatigue (91,252, 5.5%), and coughing (86,235, 5.2%). The 5 most discussed medical conditions were COVID-19 (in 5,420,276 posts or 66.4% of the total posts mentioning medical conditions), unspecified infectious disease (469,356, 5.8%), influenza (270,166, 3.3%), unspecified disorders of the central nervous system (253,407, 3.1%), and depression (151,752, 1.9%). Changes in posts in the frequency of anxiety, generalized pain, and weight loss were significant but negatively correlated with daily new COVID-19 cases in the United States (r=-0.49, r=-0.46, and r=-0.39, respectively; P<.05). Posts on the frequency of anxiety, generalized pain, weight loss, fatigue, and the changes in fatigue positively and significantly correlated with daily changes in both new deaths and new active cases in the United States (r ranged=0.39-0.48; P<.05).ConclusionsCOVID-19 and symptoms of anxiety were the 2 most commonly discussed health-related topics on social media from June 14 to December 13, 2020. Real-time monitoring of social media posts on symptoms and medical conditions may help assess the population’s mental health status and enhance public health surveillance for infectious disease.

Comparing Public Sentiment Toward COVID-19 Vaccines Across Canadian Cities: Analysis of Comments on Reddit

BackgroundSocial media enables the rapid consumption of news related to COVID-19 and serves as a platform for discussions. Its richness in text-based data in the form of posts and comments allows researchers to identify popular topics and assess public sentiment. Nonetheless, the vast majority of topic extraction and sentiment analysis based on social media is performed on the platform or country level and does not account for local culture and policies.ObjectiveThe aim of this study is to use location-based subreddits on Reddit to study city-level variations in sentiments toward vaccine-related topics.MethodsComments on posts providing regular updates on COVID-19 statistics in the Vancouver (r/vancouver, n=49,291), Toronto (r/toronto, n=20,764), and Calgary (r/calgary, n=21,277) subreddits between July 13, 2020, and June 14, 2021, were extracted. Latent Dirichlet allocation was used to identify frequently discussed topics. Sentiment (joy, sadness, fear, and anger) scores were assigned to comments through random forest regression.ResultsThe number of comments on the 250 posts from the Vancouver subreddit positively correlated with the number of new daily COVID-19 cases in British Columbia (R=0.51, 95% CI for slope 0.18-0.29; P<.001). From the comments, 13 topics were identified. Two were related to vaccines, 1 regarding vaccine uptake and the other about vaccine supply. The levels of discussion for both topics were linked to the total number of vaccines administered (Granger test for causality, P<.001). Comments pertaining to either topic displayed higher scores for joy than for other topics (P<.001). Calgary and Toronto also discussed vaccine uptake. Sentiment scores for this topic differed across the 3 cities (P<.001).ConclusionsOur work demonstrates that data from city-specific subreddits can be used to better understand concerns and sentiments around COVID-19 vaccines at the local level. This can potentially lead to more targeted and publicly acceptable policies based on content on social media.

Age, Sex, Population Density and COVID-19 Pandemic in Thailand: A Nationwide Descriptive Correlational Study

Objective: It is reported that age and sex have been identified as potential risk factors for severe outcomes and the distribution of coronavirus disease (COVID-19), although the specifics of these relationships are unclear. Furthermore, little is known about the relationship between age, sex, COVID-19, and population density in Thailand. This study proposed to examine the relationships among age, sex, population density, and the number of COVID-19 patients in Thailand.Material and Methods: In this nationwide descriptive correlational study, the dataset of daily COVID-19 cases in Thailand between January 12, 2020, and November 30, 2020, and population density (people/km2 ) in each province of Thailand was retrieved from the Open Government Data of Thailand, the Registration Office Department of the Interior, the Ministry of the Interior, and the National Statistical Office of Thailand. Chi-square and Pearson product-moment correlation were used to determine the difference and relationships among studied variables. Simple linear regression was used to predict the number of COVID-19 cases based on population density.Results: The findings illustrated a significant difference between male and female patients, in which the number of male patients was higher than female patients across age groups 31-45 years, 40-60 years, and &gt;60 years (p-value&lt;0.010). Further, population density was significantly associated with the number of COVID-19 cases.Conclusion: This investigation would provide intervention planning implications during potential future pandemics, especially in groups at higher risk (males, age 17-46 years old, and people living in high-density population areas).

Human mobility impacts on the surging incidence of COVID‐19 in India

Human mobility triggers how fast and where infectious diseases spread and modelling community flows helps assess the impact of social distancing policies and advance our understanding of community behaviour in such circumstances. This study investigated the relationship between human mobility and the surging incidence of COVID‐19 in India. We performed a generalised estimating equation with a Poisson log‐linear model to analyse the daily mobility rate and new cases of COVID‐19 between 14 March and 11 September 2020. We found that mobility to grocery and retail locations was significantly associated (p < 0.01) with the incidence of COVID‐19, these being crowded and unorganised in most parts of India. In contrast, visits to parks, workplaces, and transit stations did not considerably affect the changing COVID‐19 cases over time. In particular, workplaces equipped with social distancing protocols or low‐density open spaces are much less susceptible to the spread of the virus. These findings suggest that human mobility data, geographic information, and health geography modelling have significant potential to inform strategic decision‐making during pandemics because they provide actionable knowledge of when and where communities might be exposed to the disease.

Association of Jail Decarceration and Anticontagion Policies With COVID-19 Case Growth Rates in US Counties

Mass incarceration is known to foster infectious disease outbreaks, amplification of infectious diseases in surrounding communities, and exacerbation of health disparities in disproportionately policed communities. To date, however, policy interventions intended to achieve epidemic mitigation in US communities have neglected to account for decarceration as a possible means of protecting public health and safety. To evaluate the association of jail decarceration and government anticontagion policies with reductions in the spread of SARS-CoV-2. This cohort study used county-level data from January to November 2020 to analyze COVID-19 cases, jail populations, and anticontagion policies in a panel regression model to estimate the association of jail decarceration and anticontagion policies with COVID-19 growth rates. A total of 1605 counties with data available on both jail population and COVID-19 cases were included in the analysis. This sample represents approximately 51% of US counties, 72% of the US population, and 60% of the US jail population. Changes to jail populations and implementation of 10 anticontagion policies: nursing home visitation bans, school closures, mask mandates, prison visitation bans, stay-at-home orders, and closure of nonessential businesses, gyms, bars, movie theaters, and restaurants. Daily COVID-19 case growth rates. In the 1605 counties included in this study, the mean (SD) prison population was 283.38 (657.78) individuals, and the mean (SD) population was 315.24 (2151.01) persons per square mile. An estimated 80% reduction in US jail populations, achievable through noncarceral management of nonviolent alleged offenses and in line with average international incarceration rates, would have been associated with a 2.0% (95% CI, 0.8%-3.1%) reduction in daily COVID-19 case growth rates. Jail decarceration was associated with 8 times larger reductions in COVID-19 growth rates in counties with above-median population density (4.6%; 95% CI, 2.2%- 7.1%) relative to those below this median (0.5%; 95% CI, 0.1%-0.9%). Nursing home visitation bans were associated with a 7.3% (95% CI, 5.8%-8.9%) reduction in COVID-19 case growth rates, followed by school closures (4.3%; 95% CI, 2.0%-6.6%), mask mandates (2.5%; 95% CI, 1.7%-3.3%), prison visitation bans (1.2%; 95% CI, 0.2%-2.2%), and stay-at-home orders (0.8%; 95% CI, 0.1%-1.6%). Although many studies have documented that high incarceration rates are associated with communitywide health harms, this study is, to date, the first to show that decarceration is associated with population-level public health benefits. Its findings suggest that, among other anticontagion interventions, large-scale decarceration and changes to pretrial detention policies are likely to be important for improving US public health, biosecurity, and pandemic preparedness.

The Need for a Paradigm Shift to Person-Centered Medicine during Pandemic Times

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection has completely changed how the world looks at medicine. Unfortunately, the larger focus has been on the physical health only – epidemiology, clinical features, prevention of transmission, and management and there is very little focus on mental health and stigmatization. The 2019 coronavirus disease (COVID-19) has caused universal psychosocial impact by causing emotional disturbances, economic burden, and financial losses on a massive scale. Effects such as posttraumatic stress disorder, depression, anxiety, obsessive–compulsive symptoms, and insomnia in the post infection period have been reported among COVID-19 survivors. With disease progression, clinical symptoms become severe and infected patients may develop psychological problems. With exponential growth in the number of daily COVID-19 cases since March, 2021, India reported more than 400,000 new cases daily on May 1, 2021. India's COVID-19 surge could have become a regional disaster impacting all of south Asia. However, India has successfully avoided that disaster by strengthening the surveillance systems, imposing travel restrictions, lockdowns, and mandatory travel quarantine for individuals returning from infected areas. These were necessary to control the spread of SARS-CoV-2. The situation in India required urgent, bold measures and close cooperation between India and the global community. Currently, free vaccinations for the whole population are being given. With 1.4 billion people, this is going to be a massive effort. The pandemic and the aftermath need a paradigm shift from our traditional medical care models to one that is person centered. A person-centered model of care will be best solution here and all across the world. This is especially so, when we are fighting a disastrous pandemic.

Infodemics and its Effect on Mental Health of Medical Students of IMDC during COVID-19 Pandemic

IntroductionGlobally rising the number of daily COVID-19 cases has triggered the incredible surge in the information (and misinformation) regarding COVID-19. This misinformation was about numbers of cases, inaccurate guidelines, advertisement of unapproved treatments, and remedies as cures. The increasing prevalence of depression and anxiety was found to be linked directly to the misinformation catered from social media. The study aims to explore the mental health of medical students due to a surge in information and misinformation regarding Covid-19.Materials and MethodsThis cross-sectional study was conducted online from August 2020 to January, 2021. 227 MBBS students of IMDC (1st year to final year with age range 19-25 years) were invited to participate in an online survey through Google docs. Those who were on medication for anxiety and depression were excluded from the study. The use of the data for research purposes was explained and written consent in the first section of the online survey was given to all participants before filling the questionnaire which was divided into 3 sections. The first part was about Demographic information. The second part consisted of 5 questions about info-media use and the third part consisting of 12 questions about mental health including stress-related and behavioral changes, somatic symptoms, emotional disturbance, and cognitive functioning during the covid-19 pandemic. Ethical Approval was taken from the ethical committee of IMDC. A Chi-square test was applied to calculate the significance of qualitative variables and determine the prevalence of mental health.ResultsThe total number of students who participated in this survey were 227 (28.2% males and 71.8% females). Among types of info-media, Whats-app was used most frequently by MBBS students. Different questions were asked regarding Info-media usage and mental health. A significant association was found between gender (Both males and females) and feelings of fear and worry (p-value 0.004**), feeling socially isolated(p= 0.005**), difficulty in communicating with people (p = 0.021*), feeling confused in decision making as time progressed( 0.008**), lack of interest in studying or difficulty in concentration during this time (p 0.037*) and effect on memorizing ability (0.000)**.MBBS students who were regularly using Info-media showed a significant disturbance in mental health problems including stress-related and behavioral changes and somatic symptoms. They were found to be significantly fearful and worried (p-value 0.010*), had difficulty in communicating with people regarding covid-19(p-value 0.021*), and also felt a change in appetite-valuable 0.030*). No serious mental health problem was found related to cognitive functioning. ConclusionIn conclusion, our findings show that there is a high prevalence of mental health problems, which were positively associated with frequent use of info-media during the COVID-19 outbreak.

The Persistence of the Impact of COVID-19-Related Distress, Mood Inertia, and Loneliness on Mental Health During a Postlockdown Period in Germany: An Ecological Momentary Assessment Study.

BackgroundThe first wave of the COVID-19 pandemic in early 2020 increased mental health problems globally. However, little is known about mental health problems during a low-incidence period of the pandemic without strict public health measures.ObjectiveWe aim to investigate whether COVID-19–related risk factors for mental health problems persist beyond lockdown measures. We targeted a vulnerable population that is at risk of developing low mental health and assessed their daily dynamics of mood and emotion regulation after a strict lockdown.MethodsDuring a postlockdown period in Germany (between August 8, 2020, and November 1, 2020), we conducted an ecological momentary assessment with 131 participants who experienced at least mild COVID-19–related distress and loneliness. To estimate negative mood inertia, we built a lag-1 three-level autoregressive model.ResultsWe found that information exposure and active daily COVID-19 cases did not have an impact on negative mood amid a postlockdown period. However, there was a day-to-day carryover effect of negative mood. In addition, worrying about COVID-19, feeling restricted by COVID-19, and feeling lonely increased negative mood.ConclusionsThe mental health of a vulnerable population is still challenged by COVID-19–related stressors after the lifting of a strict lockdown. This study highlights the need to protect mental health during postpandemic periods.

Emerging application of Google Trends searches on "conjunctivitis" for tracing the course of COVID-19 pandemic.

Purpose:The aim of the present study was to use Google Trends for evaluating the association between the internet searches of the term “conjunctivitis” and the daily new cases of COVID-19.Methods:The relative search volume (RSV) of conjunctivitis from January 1 to April 16, 2019 (control group), January 1 to April 16, 2020 (first wave), and October 1 to December 31, 2020 (second wave) was obtained using Google Trends in Italy, France, United Kingdom, and United States. The number of COVID-19 daily new cases in the same countries were retrieved from Worldometer. Lag time correlation analyses were performed to evaluate the relationship between RSV and daily new cases (Pearson’s correlation coefficient).Results:During the first wave, the lagged RSV of conjunctivitis was significantly correlated with the number of COVID-19 daily new cases in all investigated countries. The highest correlation coefficients were obtained with a lag of 16 days in Italy (R = 0.868), 18 days in France (R = 0.491), 15 days in United Kingdom (R = 0.883), and 14 days in United States (R = 0.484) (all p < 0.001). Conversely, no significant correlations were found in the second wave and in the control group.Conclusion:Google Trends searches on conjunctivitis were significantly correlated with COVID-19 daily new cases during the first wave in Italy, France, United Kingdom, and United States, with a lag of 14–18 days. Repeating the analysis for the second wave, however, no significant correlations were found in any of the investigated countries.

Can climate help fighting COVID-19 trauma? A case study of Maricopa County, Arizona, USA

Introduction: Since the emergence of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus, the disease has spread rapidly throughout the world and became a traumatic stressor. Identification of the factors affecting the spread of the disease makes it possible to prevent its further propagation and save more people in similar situations. Environmental and climatic parameters are among the factors affecting the prevalence of diseases. Determination of environmental effects on Coronavirus disease (COVID-19) prevalence can help develop policies to suppress the spread. Methods: This study investigated the effect of climatic parameters on the spread of COVID-19 disease in County Maricopa from March 11, 2020, to November 30, 2020. These parameters include maximum, minimum, and mean daily temperature as well as maximum, minimum, and mean daily humidity, wind speed, solar radiation, and Air Quality Index (AQI) of particulate matter10 (PM10), PM2.5, and O3. A Shapiro-Wilk test was used to evaluate the normality of variables and the Spearman correlation test was used to determine the correlation between parameters and daily COVID-19 cases. A simple linear regression was applied on parameters that had significant Spearman’sranked correlation with the daily COVID-19 cases to determine their contribution to the pandemic. Results: The present study showed that the maximum, minimum, and mean temperature parameters and PM10 and PM2.5 particles had a positive and significant correlation (P&lt;0.01) with the prevalence of COVID-19 disease. The effect of PM10 particles was higher than the other parameters (0.488, P&lt;0.01). The parameters of maximum, minimum, and mean relative humidity along with solar radiation and O3 AQI had a significant and negative correlation with the development of COVID-19 disease (P&lt;0.01). The effect of maximum humidity was higher than that of the other parameters (-0.364, P&lt;0.01). A linear regression test showed that O3 (β=-15.16, P&lt;0.001) and Tmean (β=18.47, P&lt;0.01) significantly predicted daily COVID-19 cases. Conclusion: It can be concluded that climatic parameters can affect the COVID-19 pandemic and should be addressed.

On Development of MySignals based prototype for application in health vitals monitoring.

India's health infrastructure is under pressure since the daily COVID-19 cases have crossed the milestone of 4 Lakhs cases per day which surpass the previous years' peak. Patients with mild symptoms have been advised for home treatment since most of the hospitals are running out of bed. In this situation delivering healthcare to people has become revolutionizing due to the rapid advancement of embedded systems, communication, and informatics technologies. Integration of different health sensors, handheld devices, and internet can be a great potential for significant improvement of the quality of remote healthcare. This paper discusses the use of MySignals HW shield which is a hardware development platform for medical devices to build e-health monitoring system. Wearable health monitoring system prototype has been developed in this work. To conduct experiments, health vitals such as body temperature, ECG, oxygen saturation level, and pulse rate from 5 volunteers have been measured, collected, and stored in a cloud database using the system prototype. To evaluate the performance of the prototype, transmission delay has been recorded in both wired (Ethernet) and wireless (Wi-Fi) communication modes. It is observed that it takes 2.71 ms and 5.18 ms of time to collect and store the health vitals to the cloud database in wired and wireless mode respectively. Comparing the collected health vitals with the normal range of health vitals, no abnormality is found in all volunteer's health. Finally, a framework for contactless monitoring of COVID-affected patients is proposed. Contactless monitoring of health vitals can reduce the chance of community spread.

Association of Limited In-Person Attendance in US National Football League and National Collegiate Athletic Association Games With County-Level COVID-19 Cases

In 2020 and early 2021, the National Football League (NFL) and National Collegiate Athletic Association (NCAA) opted to host football games in stadiums across the country. The in-person attendance of games varied with time and from county to county. There is currently no evidence on whether limited in-person attendance of games is associated with COVID-19 case numbers on a county-level. To assess whether NFL and NCAA football games with limited in-person attendance were associated with increased COVID-19 cases in the counties they were held compared with a matched set of counties. In this time-series cross-sectional study, every county hosting NFL or NCAA games with in-person attendance (treated group) in 2020 and 2021 was matched with a county that that did not host a game on the corresponding day but had an identical game history for up to 14 days prior (control group). A standard matching method was used to further refine this matched set so that the treated and matched control counties had similar population size, nonpharmaceutical interventions in place, and COVID-19 trends. The association of hosting games with in-person attendance with COVID-19 cases was assessed using a difference-in-difference estimator. Data were analyzed from August 29 to December 28, 2020. Hosting NFL or NCAA games. The main outcome was estimation of new COVID-19 cases per 100 000 residents at the county level reported up to 14 days after a game among counties with NFL and NCAA games with in-person attendance. A total of 528 games with in-person attendance (101 NFL games [19.1%]; 427 NCAA games [80.9%]) were included. The matching algorithm returned 361 matching sets of counties. The median (interquartile range [IQR]) number of attendance for NFL games was 9949 (6000 to 13 797) people. The median number of attendance for NCAA games was not available, and attendance was recorded as a binary variable. The median (IQR) daily new COVID-19 cases in treatment group counties hosting games was 26.14 (10.77-50.25) cases per 100 000 residents on game day. The median (IQR) daily new COVID-19 cases in control group counties where no games were played was 24.11 (9.64-48.55) cases per 100 000 residents on game day. The treatment effect size ranged from -5.17 to 4.72, with a mean (SD) of 1.21 (2.67) cases per 100 000 residents, within the 14-day period in all counties hosting the games, and the daily treatment effect trend remained relatively steady during this period. This cross-sectional study did not find a consistent increase in the daily COVID-19 cases per 100 000 residents in counties where NFL and NCAA games were held with limited in-person attendance. These findings suggest that NFL and NCAA football games hosted with limited in-person attendance were not associated with substantial risk for increased local COVID-19 cases.

Phased Implementation of COVID-19 Vaccination: Rapid Assessment of Policy Adoption, Reach and Effectiveness to Protect the Most Vulnerable in the US.

The US and the rest of the world have suffered from the COVID-19 pandemic for over a year. The high transmissibility and severity of this virus have provoked governments to adopt a variety of mitigation strategies. Some of these previous measures, such as social distancing and mask mandates, were effective in reducing the case growth rate yet became economically and administratively difficult to enforce as the pandemic continued. In late December 2020, COVID-19 vaccines were first approved in the US and states began a phased implementation of COVID-19 vaccination. However, there is limited quantitative evidence regarding the effectiveness of the phased COVID-19 vaccination. This study aims to provide a rapid assessment of the adoption, reach, and effectiveness of the phased implementation of COVID-19 vaccination. We utilize an event-study analysis to evaluate the effect of vaccination on the state-level daily COVID-19 case growth rate. Through this analysis, we assert that vaccination was effective in reducing the spread of COVID-19 shortly after the first shots were given. Specifically, the case growth rate declined by 0.124, 0.347, 0.345, 0.464, 0.490, and 0.756 percentage points corresponding to the 1–5, 6–10, 11–15, 16–20, 21–25, and 26 or more day periods after the initial shots. The findings could be insightful for policymakers as they work to optimize vaccine distribution in later phases, and also for the public as the COVID-19 related health risk is a contentious issue.

A study on the effects of meteorological and climatic factors on the COVID-19 spread in Canada during 2020.

Graphical abstractThe Coronavirus (COVID-19) pandemic has infected more than three million people, with thousands of deaths and millions of people into quarantine. In this research, the authors focus on meteorological and climatic factors on the COVID-19 spread, the main parameters including daily new cases of COVID-19, carbon dioxide (CO2) emission, nitrogen dioxide (NO2), Sulfur dioxide (SO2), PM2.5, Ozone (O3), average temperature, and humidity are examined to understand how different meteorological parameters affect the COVID-19 spread in Canada? The graphical quantitative analysis results indicate that CO2 emissions, air quality, temperature, and humidity have a direct negative relationship with COVID-19 infections. Quantile regression analysis revealed that air quality, Nitrogen, and Ozone significantly induce the COVID-19 spread across Canadian provinces. The findings of this study are contrary to the earlier studies, which argued that weather and climate change significantly increase COVID-19 infections. We suggested that meteorological and climatic factors might be critical to reducing the COVID-19 new cases in Canada based on the findings. This work’s empirical conclusions can provide a guideline for future research and policymaking to stop the COVID-19 spread across Canadian provinces.

COVID-19 European regional tracker

This Tracker presents data on daily COVID-19 cases at the sub-national level for 26 European countries from January 2020 till present. Country-level data sources are identified and processed to form a homogenized panel at the NUTS 3 or NUTS 2 level, the two lowest standardized administrative units in Europe. The strengths and weaknesses of each country dataset are discussed in detail. The raw data, spatial layers, the code, and the final homogenized files are provided in an online repository for replication. The data highlights the spatial distribution of cases both within and across countries that can be utilized for a disaggregated analysis on the impacts of the pandemic. The Tracker is updated monthly to expand its coverage.

COVID-19 CASES, MEDIA ATTENTION AND SOCIAL MOOD

The study uses rolling regressions, both at global and country level, to analyze the impact of daily Covid-19 case numbers on four (Panic, Sentiment, Media coverage, and Fake news) indices. The indices are obtained from the Ravenpack Finance, while the daily Covid-19 cases and the policy response stringency index data is extracted from the Oxford COVID-19 Government Response Tracker. The results indicate that the impact of the number of daily Covid-19 cases on the indices is quite variable over time. Higher impact on the indices is reflected in periods where there is a significant surge in cases, in particular the initial surge in Spring, Summer and Fall. There is some evidence of diminished (increased) sensitivity of panic and media indices (fake news) to number of cases but this is not consistent across all countries. These results indicate that the public are concerned and respond to changes in the trends of the spread of the virus and highlight the importance of managing trends if halting its spread is not immediately feasible.Keywords: COVID-19, Ravenpack Indices, Media Attention, Stringency IndexJEL Classifications: G40, G41DOI: https://doi.org/10.32479/ijefi.11591